Secondly, a different variation may happen in the data distribution. Indeed, the label distribution might drift. In other words, a given sample may have various label through time. This type of drift is called real concept drift [1]}, [2]}.
In this scenario, the model has to choose between learning the various labels for one input or to forget past labels to only remember the current label and be up to date.
